{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "start=time.time()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n",
      "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n",
      "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n",
      "Requirement already satisfied: pip in ./.local/lib/python3.7/site-packages (21.1.2)\n"
     ]
    }
   ],
   "source": [
    "! pip install --upgrade  --user pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.7.3 (default, Mar 27 2019, 22:11:17) \\n[GCC 7.3.0]'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys \n",
    "sys.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n",
      "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n",
      "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n",
      "Requirement already satisfied: tensorflow in ./.local/lib/python3.7/site-packages (2.5.0)\n",
      "Requirement already satisfied: google-pasta~=0.2 in ./.local/lib/python3.7/site-packages (from tensorflow) (0.2.0)\n",
      "Requirement already satisfied: protobuf>=3.9.2 in ./.local/lib/python3.7/site-packages (from tensorflow) (3.17.3)\n",
      "Requirement already satisfied: gast==0.4.0 in ./.local/lib/python3.7/site-packages (from tensorflow) (0.4.0)\n",
      "Requirement already satisfied: keras-nightly~=2.5.0.dev in ./.local/lib/python3.7/site-packages (from tensorflow) (2.5.0.dev2021032900)\n",
      "Requirement already satisfied: termcolor~=1.1.0 in ./.local/lib/python3.7/site-packages (from tensorflow) (1.1.0)\n",
      "Requirement already satisfied: numpy~=1.19.2 in ./.local/lib/python3.7/site-packages (from tensorflow) (1.19.5)\n",
      "Requirement already satisfied: tensorboard~=2.5 in ./.local/lib/python3.7/site-packages (from tensorflow) (2.5.0)\n",
      "Requirement already satisfied: h5py~=3.1.0 in ./.local/lib/python3.7/site-packages (from tensorflow) (3.1.0)\n",
      "Requirement already satisfied: opt-einsum~=3.3.0 in ./.local/lib/python3.7/site-packages (from tensorflow) (3.3.0)\n",
      "Requirement already satisfied: typing-extensions~=3.7.4 in ./.local/lib/python3.7/site-packages (from tensorflow) (3.7.4.3)\n",
      "Requirement already satisfied: astunparse~=1.6.3 in ./.local/lib/python3.7/site-packages (from tensorflow) (1.6.3)\n",
      "Requirement already satisfied: flatbuffers~=1.12.0 in ./.local/lib/python3.7/site-packages (from tensorflow) (1.12)\n",
      "Requirement already satisfied: grpcio~=1.34.0 in ./.local/lib/python3.7/site-packages (from tensorflow) (1.34.1)\n",
      "Requirement already satisfied: wrapt~=1.12.1 in ./.local/lib/python3.7/site-packages (from tensorflow) (1.12.1)\n",
      "Requirement already satisfied: six~=1.15.0 in ./.local/lib/python3.7/site-packages (from tensorflow) (1.15.0)\n",
      "Requirement already satisfied: absl-py~=0.10 in ./.local/lib/python3.7/site-packages (from tensorflow) (0.12.0)\n",
      "Requirement already satisfied: keras-preprocessing~=1.1.2 in ./.local/lib/python3.7/site-packages (from tensorflow) (1.1.2)\n",
      "Requirement already satisfied: tensorflow-estimator<2.6.0,>=2.5.0rc0 in ./.local/lib/python3.7/site-packages (from tensorflow) (2.5.0)\n",
      "Requirement already satisfied: wheel~=0.35 in ./.local/lib/python3.7/site-packages (from tensorflow) (0.36.2)\n",
      "Requirement already satisfied: cached-property in ./.local/lib/python3.7/site-packages (from h5py~=3.1.0->tensorflow) (1.5.2)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in ./.local/lib/python3.7/site-packages (from tensorboard~=2.5->tensorflow) (1.8.0)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in /opt/tljh/user/lib/python3.7/site-packages (from tensorboard~=2.5->tensorflow) (2.22.0)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in ./.local/lib/python3.7/site-packages (from tensorboard~=2.5->tensorflow) (0.6.1)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /opt/tljh/user/lib/python3.7/site-packages (from tensorboard~=2.5->tensorflow) (41.0.1)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in ./.local/lib/python3.7/site-packages (from tensorboard~=2.5->tensorflow) (0.4.4)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in ./.local/lib/python3.7/site-packages (from tensorboard~=2.5->tensorflow) (2.0.1)\n",
      "Requirement already satisfied: google-auth<2,>=1.6.3 in ./.local/lib/python3.7/site-packages (from tensorboard~=2.5->tensorflow) (1.30.2)\n",
      "Requirement already satisfied: markdown>=2.6.8 in ./.local/lib/python3.7/site-packages (from tensorboard~=2.5->tensorflow) (3.3.4)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in ./.local/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow) (4.2.2)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in ./.local/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow) (4.7.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in ./.local/lib/python3.7/site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow) (0.2.8)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in ./.local/lib/python3.7/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow) (1.3.0)\n",
      "Requirement already satisfied: importlib-metadata in /opt/tljh/user/lib/python3.7/site-packages (from markdown>=2.6.8->tensorboard~=2.5->tensorflow) (4.4.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in ./.local/lib/python3.7/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow) (0.4.8)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /opt/tljh/user/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow) (2.8)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/tljh/user/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow) (3.0.4)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/tljh/user/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow) (1.24.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/tljh/user/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow) (2021.5.30)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/tljh/user/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.5->tensorflow) (3.1.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/tljh/user/lib/python3.7/site-packages (from importlib-metadata->markdown>=2.6.8->tensorboard~=2.5->tensorflow) (3.4.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install --user  tensorflow "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n",
      "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n",
      "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n",
      "Requirement already satisfied: matplotlib in ./.local/lib/python3.7/site-packages (3.4.2)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /opt/tljh/user/lib/python3.7/site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: pillow>=6.2.0 in ./.local/lib/python3.7/site-packages (from matplotlib) (8.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/tljh/user/lib/python3.7/site-packages (from matplotlib) (2.8.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in ./.local/lib/python3.7/site-packages (from matplotlib) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in ./.local/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: numpy>=1.16 in ./.local/lib/python3.7/site-packages (from matplotlib) (1.19.5)\n",
      "Requirement already satisfied: six in ./.local/lib/python3.7/site-packages (from cycler>=0.10->matplotlib) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install --user matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5.0\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(train_images.shape)\n",
    "\n",
    "print(train_images[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = train_images[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5, 5, i + 1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(128, activation='relu'),\n",
    "    keras.layers.Dense(10)\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5005 - accuracy: 0.8241\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3748 - accuracy: 0.8629\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3353 - accuracy: 0.8768\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3109 - accuracy: 0.8864\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2944 - accuracy: 0.8910\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2786 - accuracy: 0.8973\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2675 - accuracy: 0.9001\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2557 - accuracy: 0.9051\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2476 - accuracy: 0.9069\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2379 - accuracy: 0.9120\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f3208e30dd8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 0s - loss: 0.3353 - accuracy: 0.8838\n",
      "\n",
      "Test accuracy: 0.8838000297546387\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "probability_model = tf.keras.Sequential([model,\n",
    "                                         tf.keras.layers.Softmax()])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = probability_model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.6971360e-07 2.9497649e-10 7.1599016e-09 3.0021777e-10 1.0301231e-09\n",
      " 3.1086165e-03 2.6091795e-09 4.5848995e-02 1.1441671e-07 9.5104212e-01]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(predictions[0])\n",
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "    predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "\n",
    "    plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "    predicted_label = np.argmax(predictions_array)\n",
    "    if predicted_label == true_label:\n",
    "        color = 'blue'\n",
    "    else:\n",
    "        color = 'red'\n",
    "\n",
    "    plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                         100 * np.max(predictions_array),\n",
    "                                         class_names[true_label]),\n",
    "               color=color)\n",
    "\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "    predictions_array, true_label = predictions_array, true_label[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks(range(10))\n",
    "    plt.yticks([])\n",
    "    thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "    plt.ylim([0, 1])\n",
    "    predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "    thisplot[predicted_label].set_color('red')\n",
    "    thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 12\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n",
      "(1, 28, 28)\n",
      "[[3.9948031e-06 6.1040197e-15 9.9526477e-01 7.9600848e-10 3.4473601e-04\n",
      "  2.5891591e-11 4.3864772e-03 4.9966654e-16 1.6314259e-09 4.7292162e-15]]\n",
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grab an image from the test dataset.\n",
    "img = test_images[1]\n",
    "\n",
    "print(img.shape)\n",
    "\n",
    "\n",
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)\n",
    "\n",
    "predictions_single = probability_model.predict(img)\n",
    "\n",
    "print(predictions_single)\n",
    "\n",
    "\n",
    "plot_value_array(1, predictions_single[0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)\n",
    "\n",
    "print(np.argmax(predictions_single[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45.59731864929199\n"
     ]
    }
   ],
   "source": [
    "end=time.time()\n",
    "print(end-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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